The field of medical imaging has seen significant advances since the time X-Rays were first used to determine anatomical abnormalities. Medical imaging hardware has progressed in the form of newer machines such as Medical Resonance Imaging (MRI) scanners, Computed Axial Tomography (CAT) scanners, etc. Because of large amount of image data generated by such modern medical scanners, there has been and remains a need for developing image processing techniques that can automate some or all of the processes to determine the presence of anatomical abnormalities in scanned medical images.
Digital medical images are constructed using raw image data obtained from a scanner, for example, a CAT scanner, MRI, etc. Digital medical images are typically either a two-dimensional (“2-D”) image made of pixel elements or a three-dimensional (“3-D”) image made of volume elements (“voxels”). Such 2-D or 3-D images are processed using medical image recognition techniques to determine the presence of anatomical structures such as cysts, tumors, polyps, etc. Given the amount of image data generated by any given image scan; it is preferable that an automatic technique should point out anatomical features in the selected regions of an image to a doctor for further diagnosis of any disease or condition. Automatic image processing and recognition of structures within a medical image is generally referred to as Computer-Aided Detection (CAD). A CAD system can process medical images and identify anatomical structures including possible abnormalities for further review. Such possible abnormalities are often called candidates and are considered to be generated by the CAD system based upon the medical images.
CAD techniques have emerged as powerful tools for detecting colonic polyps in three-dimensional (3D) Computed Tomography Colonography (CTC) or virtual colonoscopy. 3D CTC is a noninvasive and effective tool for early detection of polyps, which are growths or bumps on the colorectal lining that usually indicate the presence of colon cancer. Colon cancer is the second leading cause of cancer death in western countries, but it is one of the most preventable of cancers because doctors can identify and remove its precursor known as a polyp. To enhance polyp findings in collapsed or fluid-tagged colon segments, and better distinguish polyps from pseudo polyps (e.g. tagged stools), the current CTC practice is to obtain two scans of a patient in prone and supine positions respectively. This allows the radiologist to not only see areas that may not be visible in the other scan, but also to assess the mobility of a finding. Any true polyp will not move within the colon, whereas pseudo polyps tend to shift when the position of the patient is changed. However, the colon can move and deform significantly between the prone and supine scans, which makes it difficult to assess whether a polyp or pseudo polyp has moved within the colon. Manual registration of polyp findings or colon segments is also difficult, inaccurate and time-consuming.
It is crucial that a polyp detection system and method have high sensitivity to true polyps. At the same time, it is extremely beneficial if the detection system minimizes the number of false positives detected. The ultimate goal is a system that can detect 100% of all malignant polyps (100% sensitive) while detecting zero false positive polyps. Current systems can reach approximately 88.9% sensitivity with 3.81 false positive (FP) rate per patient during CAD polyp detection. While these detection rates are a marked improvement over older systems, the less than 100% sensitivity and the moderate number of false positives detected still present a significant problem in providing sufficient early detection.
Therefore, there is a need for improved systems and methods for detecting polyps with maximum sensitivity and minimum false positives, and for assessing polyps by helping the radiologist to identify corresponding CAD findings across various views.